多源地学信息在土地荒漠化遥感分类中的应用研究

Research on Multi-Source Geographic Information Based Classification of Desertification

土地荒漠化是当今全球面临的重大环境问题之一 ,它的发生、发展及其逆转是气候、环境和人类社会经济活动综合作用的结果 .荒漠化土地分类是土地荒漠化研究中至关重要的一个环节 ,由于地形粗糙度和植被覆盖率对土地荒漠化具有重要的影响 ,但遥感图象很难对地表地形粗糙度进行定量描述 ,因此引入了数字高程模型(DEM)对研究区地形粗糙度进行表达和采用植被指数 (NDVI)对地面植被覆盖率进行描述 ,并对由DEM生成的地面坡度 (SLOPE)图象、植被指数图象与原始遥感图象进行了信息融合 ,从而大大丰富了遥感图象的荒漠化信息 .通过对融合图象的n 维散度分析 ,不同类型、不同程度的土地荒漠化样本的分离度大幅度提高 .分类结果也证明 ,信息融合可大大提高分类精度 .

Desertification is one of the most serious environment problems in the world today, The generation, development and reversion of desertification are caused by the results of comprehensive influences from the climatic and environmental change, and human activities. The classification of desertification is one of the key steps in desertification research. Terrain roughness and vegetation growth are important influence factors of desertification. But it is very difficult to describe the terrain roughness in remote sensing image. In this thesis digital elevation model(DEM) was used to express the Terrain roughness of research area and normalized difference vegetation index(NDVI) were used to describe the vegetation growth. Terrain slope(SLOPE) image (produced by DEM) and NDVI image (produced by TM image) were used to fuse with original TM image. The fusion image greatly enriched the desertification information. According to n- dimension separation analysis to fusion image, the separation in templates of different kinds and degrees of desertification was increased greatly. The classification result shows that the accurate of classification was greatly improved through information fusion.